class_docstr: Extracts out the task_id from the first index of each example for each task.
  Then splits the input and performs tokenization
  Params:
  
  vocab_file(str): vocabulary file for tokenization
  do_lower_case(bool): if True, tokenization is lower-cased. Default: True
  preprocessor(str): name of DeepPavlov class that is used for tokenization. 
  Default: TorchTransformersPreprocessor
  preprocessors(List[str]): list of names of DeepPavlov classes that are used for tokenization.
  Overrides preprocessor . The length of list must be equal to the number of tasks
  max_seq_length(int): Maximum sequence length for tokenizer. Default: 512
  strict(bool): if True, we always try to split data assuming predefined modes as in multitask_example.json  
  If False, we go without splitting if we are not sure how to split the data. Default: False
  print_first_example(bool): if True, we print the first input example after initialization. Default: False 
code: def __init__(
      self,
      vocab_file,
      do_lower_case: bool = True,
      preprocessor: str = "TorchTransformersPreprocessor",
      preprocessors: List[str] = None,
      max_seq_length: int = 512,
      strict=False,
      print_first_example=False,
      *args,
      **kwargs,
  ):
      self.strict = strict
      self.printed = False
      self.print_first_example = print_first_example
      self.prefix = ""
      if preprocessors is None:
          log.info(
              f"Assuming the same preprocessor name for all : {preprocessor}"
          )
          self.preprocessor = eval(preprocessor)(
              vocab_file, do_lower_case, max_seq_length, *args, **kwargs
          )
          self.preprocessors = None
      else:
          for i in range(len(preprocessors)):
              preprocessors[i] = eval(preprocessors[i])
          self.n_task = len(preprocessors)
          self.preprocessors = [
              preprocessors[i](
                  vocab_file=vocab_file,
                  do_lower_case=do_lower_case,
                  max_seq_length=max_seq_length,
                  *args,
                  **kwargs,
              )
              for i in range(len(preprocessors))
          ]
  
file_path: DeepPavlov/deeppavlov/models/preprocessors/multitask_preprocessor.py
imports: from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Iterable
  from logging import getLogger
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.component import Component
  from deeppavlov.models.preprocessors.torch_transformers_preprocessor import *
label_desc: Use image node_id 1 to create a new MultiTaskPipelinePreprocessor object from inherited base classes: Component with example: obj = MultiTaskPipelinePreprocessor(vocab_file, do_lower_case, preprocessor, preprocessors, max_seq_length, strict, print_first_example)